AI ROI: What Fortune 100 Procurement Leaders Are Seeing

Enterprise AI spend is growing up fast, and some of the growing pains are starting to hurt.

In a recent conversation with 15 IT procurement leaders from Fortune 100 companies, NPI asked what they’re seeing as AI moves deeper into enterprise operations. The discussion covered ROI, consumption commitments, hardware costs, new commercial models and the skills procurement teams need as AI buying accelerates.

What we heard was less about where AI might be headed and more about what’s happening right now. Commitments are being burned through months earlier than expected. Discounts are disappearing when usage exceeds forecasts. Vendors can’t always explain what drives the bill. Hardware orders placed ahead of price increases are being repriced anyway.

At the same time, procurement teams are finding some approaches that work. Here are seven takeaways that stood out.

1. AI ROI is Still Pretty Fuzzy

Everyone wants to talk about AI ROI. Measuring it is a different beast. About 53% of the procurement leaders in our discussion said their organizations are tracking AI usage, while 40% are beginning to connect usage with output.

The biggest obstacle? Nobody defined the desired outcome at the beginning. When asked about their biggest ROI measurement challenge, 40% pointed to undefined outcomes, 27% lacked a baseline and 20% said they couldn’t isolate AI’s impact.

There’s also a cost problem hiding in the ROI equation. Token and consumption spend get most of the attention, but they’re only part of the bill. AI adoption can bring additional infrastructure, governance, security and control costs with it. As one procurement leader pointed out during the discussion, that adjacent spend is often missing from the ROI math.

For procurement, that puts more weight on the questions asked before the purchase:

• What outcome are we trying to achieve?
• What does the process cost today?
• How will we measure improvement?
• What other costs will this AI investment create?

If those answers don’t exist going into the deal, proving ROI later gets a lot harder.

2. The Best AI ROI Stories Are Surprisingly Small

One of the clearest ROI examples shared during the discussion didn’t involve a sweeping AI transformation.

An accountant built an agent using Claude and Copilot to replace a manual month-end accrual consolidation process. The task had taken roughly 10 hours each month. Now AI generates the output and the employee validates it, and the approach has been rolled out across the accounting team.

The math works because it’s simple. There was a defined task, a known amount of labor and a measurable change after AI was introduced.

The idea also came from an internal contest for the best AI use case, which points to something procurement leaders may want to consider. Instead of chasing an enterprise-wide answer to “What’s our AI ROI?”, look for repeatable workflows where the before-and-after comparison is obvious.

Those smaller wins can give organizations the evidence they need to make smarter decisions about where AI investment should go next.

3. A Two-Year AI commitment Could Be a Five-Month Problem

Companies are learning the hard way that overages need as much negotiation as anything else in the AI contract.

One company signed a two-year AI consumption commitment in mid-March. By August, it was gone. Once the committed volume was exhausted, the negotiated discount went with it. The company found itself back at the negotiating table, except this time the vendor knew exactly how much the customer was consuming.

Another participant described dealing with a similar issue.

This is where AI commitments can get dangerous. The discount may look attractive at signing, but if the consumption model is wrong, the customer can end up renegotiating with established usage, dependent users and very little time.

One procurement leader described another twist: after the company exceeded its commitment, the vendor treated the higher consumption level as the new baseline. There was no additional discount for the added volume because that growth was now considered expected.

The takeaway for procurement is important: negotiate the overage as carefully as the commitment.

Before signing, know what happens when consumption reaches 100%, 120% or 150% of forecast:

• What rate applies?
• Does the original discount survive?
• Are there predefined growth bands?
• What triggers a renegotiation?

With AI, the scenario you thought was an edge case can arrive surprisingly fast.

4. If Your Vendor Can’t Explain the Meter, Don’t Bet Big on the Forecast

AI pricing models are introducing a growing collection of tokens, credits, agents, hosts and consumption units. Understanding what actually generates a charge can be harder than it sounds.

One procurement leader spent more than two hours with a vendor trying to determine which agent classes carried a cost. The vendor couldn’t provide a satisfactory answer. Its own portal AI also calculated a host count roughly twice the customer’s actual engineering population.

The eventual solution was refreshingly practical: run it live for a month and see what gets billed.

That may be a useful model for other AI buyers. If the billable unit is unclear, a three-year forecast built on that unit isn’t going to become more reliable just because the spreadsheet is detailed.

Push for a pilot, measurement period or short-term arrangement that lets you observe real consumption before making a major commitment. Better usage data can be worth far more than a bigger discount against the wrong forecast.

5. Buying Hardware Early May Not Protect Price

You can’t talk about AI without talking about hardware.

One procurement leader described placing hardware orders early specifically to get ahead of announced price increases. The strategy made sense. The problem was that the orders weren’t fulfilled. The customer was eventually told the equipment would not be delivered unless the orders were repriced and resubmitted.

That raises an important question for any procurement team trying to buy ahead of hardware inflation: Does your price protection survive until fulfillment?

An early PO doesn’t necessarily protect the budget if the supplier retains the ability to reprice before delivery. With AI infrastructure demand putting pressure on parts of the hardware market, buyers need to look closely at how long quoted prices are valid, what happens when fulfillment is delayed and which contractual protections actually lock the price.

AI spend is much bigger than the AI invoice. Hardware, infrastructure and other adjacent costs belong in the conversation too.

6. Traditional Volume Discounts Are Harder to Apply

AI also creates an awkward question for procurement: what do you negotiate when volume itself is difficult to predict?

Some companies are looking beyond traditional volume discounts. In our discussion, 60% of participants said they’re exploring value-based commercial structures to address AI-driven cost increases.

Value-based models aren’t automatically better for the buyer. They require clear definitions, measurable outcomes and firm boundaries around how much value the vendor gets to capture. Procurement also needs finance and the business involved early enough to agree on those terms.

But as AI pricing becomes more consumption- and outcome-oriented, procurement teams will need more options than “buy more, get a bigger discount.”

7. Procurement Teams Are Learning AI By Doing AI

The final topic was talent, and one result stood out: roughly 85% of respondents identified AI fluency as the biggest area their teams need to develop.

One organization created a procurement AI committee staffed by employees already enthusiastic about AI. They’re building prompt libraries, demonstrating use cases and teaching their peers. Other companies have created AI champion programs, run hackathons and agent-building training, and added AI skills and tool knowledge to procurement job descriptions.

Some are going further, including AI usage in performance metrics or hiring roles specifically focused on AI tooling within procurement operations.

There’s a lesson here for procurement leaders trying to figure out how much AI training to buy. Some of the strongest examples we heard were homegrown.

Give people access to the tools. Give them real procurement problems to solve. Then create a way for the people finding useful applications to teach everyone else.

The next AI deal will benefit from what buyers are learning now

What made this conversation interesting wasn’t a big prediction about where enterprise AI is going, but the specificity of what procurement teams are encountering today.

A two-year commitment can disappear in five months. A vendor may struggle to explain the unit it wants you to commit to. Buying hardware ahead of an increase may not protect the price. And some of the easiest AI ROI to prove may come from an accountant automating 10 hours of monthly work.

These are the growing pains of a market where adoption is moving faster than many of the commercial practices around it.

For IT procurement, that makes the learning happening right now especially valuable. Test consumption assumptions before making large commitments. Negotiate what happens when the forecast is wrong. Define the outcome before trying to calculate ROI. Look beyond the AI invoice for the true cost. And give procurement teams room to experiment with the technology themselves.